singIST: an integrative method for comparative single-cell transcriptomics between disease models and humans
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Motivation
Disease models are a fundamental tool to drug discovery and early drug development. However, these models are not a perfect reflection of human disease, and selecting a suitable model can be challenging. Current computational methods to molecularly validate their pathophysiological resemblance to the human condition at the single-cell level are limited. Although quantitative computational methods exist to inform this selection, they are very limited at the single-cell resolution, which can be critical for model selection. Quantifying the resemblance of disease models to the human condition with single-cell technologies in an explainable, integrative, and generalizable manner remains a significant challenge.
Results
We developed singIST, a computational method for single-cell comparative transcriptomics analysis between disease models and humans. singIST provides explainable quantitative measures on disease model similarity to human condition at both pathway and cell type levels, highlighting the importance of each gene in the latter. These measures account for orthology, cell type presence in the disease model, cell type and gene importance in human condition, and gene changes in the disease model measured as fold change. This is achieved within a unifying framework that controls for the intrinsic complexities of single-cell data. We test our method with three well-characterized murine models of moderate to severe Atopic Dermatitis, across common deregulated pathways, for which singIST assessment recovers known facts and propose hypothesis via novel predictions.
Availability and implementation
Source code at https://github.com/amoruno/singIST-reproducibility
